当前位置: X-MOL 学术arXiv.cs.CY › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Distributed Application of Guideline-Based Decision Support through Mobile Devices: Implementation and Evaluation
arXiv - CS - Computers and Society Pub Date : 2021-02-22 , DOI: arxiv-2102.11314
Erez Shalom, Ayelet Goldstein, Elior Ariel, Moshe Sheinberger, Valerie Jones, Boris Van Schooten, Yuval Shahar

Traditionally Guideline(GL)based Decision Support Systems (DSSs) use a centralized infrastructure to generate recommendations to care providers. However, managing patients at home is preferable, reducing costs and empowering patients. We aimed to design, implement, and demonstrate the feasibility of a new architecture for a distributed DSS that provides patients with personalized, context-sensitive, evidence based guidance through their mobile device, and increases the robustness of the distributed application of the GL, while maintaining access to the patient longitudinal record and to an up to date evidence based GL repository. We have designed and implemented a novel projection and callback (PCB) model, in which small portions of the evidence based GL procedural knowledge, adapted to the patient preferences and to their current context, are projected from a central DSS server, to a local DSS on the patient mobile device that applies that knowledge. When appropriate, as defined by a temporal pattern within the projected plan, the local DSS calls back the central DSS, requesting further assistance, possibly another projection. Thus, the GL specification includes two levels: one for the central DSS, one for the local DSS. We successfully evaluated the PCB model within the MobiGuide EU project by managing Gestational Diabetes Mellitus patients in Spain, and Atrial Fibrillation patients in Italy. Significant differences exist between the two GL representations, suggesting additional ways to characterize GLs. Mean time between the central and local interactions was quite different for the two GLs: 3.95 days for gestational diabetes, 23.80 days for atrial fibrillation. Most interactions, 83%, were due to projections to the mDSS. Others were data notifications, mostly to change context. Robustness was demonstrated through successful recovery from multiple local DSS crashes.

中文翻译:

通过移动设备的基于指南的决策支持的分布式应用:实施和评估

传统上,基于指南(GL)的决策支持系统(DSS)使用集中式基础结构来生成对护理提供者的建议。但是,在家中管理患者是可取的,这样可以降低成本并赋予患者权力。我们旨在为分布式DSS设计,实施和演示新架构的可行性,该新DSS架构可通过其移动设备为患者提供个性化,上下文相关,基于证据的指导,并提高GL分布式应用程序的健壮性,而维护对患者纵向记录和基于证据的最新GL存储库的访问。我们设计并实现了一种新颖的投影和回调(PCB)模型,其中一小部分基于证据的GL程序知识适用于患者的偏好及其当前情况,从中央DSS服务器投影到应用该知识的患者移动设备上的本地DSS。在适当的时候,如在计划的计划中的时间模式所定义的那样,本地DSS会回呼中央DSS,请求进一步的协助,可能还会进行另一次投影。因此,GL规范包括两个级别:一个级别用于中央DSS,一个级别用于本地DSS。我们通过管理西班牙的妊娠糖尿病患者和意大利的房颤患者,成功地在MobiGuide EU项目中评估了PCB模型。两种GL表示之间存在显着差异,这建议了表征GL的其他方法。两种GL的中枢和局部相互作用之间的平均时间有很大不同:妊娠糖尿病为3.95天,房颤为23.80天。大多数交互(83%​​)是由于对mDSS的预测。其他是数据通知,主要是为了更改上下文。通过从多个本地DSS崩溃中成功恢复,证明了其鲁棒性。
更新日期:2021-02-24
down
wechat
bug